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Put the approval gate on the tool that causes the side effect—not on the agent in the abstract. A send-message, purchase, transfer, refund, cancellation, or payment tool should pause the run before execution, return the proposed call to your application, and wait for a reviewer to approve or reject it. The reviewer should see the exact arguments, destination, amount, limits, and policy context. After the decision is recorded, resume the same run from its preserved state. If the approval service is unavailable or the decision cannot be verified, fail closed and do not execute the call.
This design works for a single agent, handoffs, and nested agent tools. The important details are where the gate runs, how rules decide which calls need review, how state survives a delay, and how you prevent duplicate execution when a run is retried.
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Require approval immediately before the tool that creates the external effect. An agent-level instruction such as “ask me before doing anything important” is not a reliable security boundary: a handoff, nested agent, or newly added tool can bypass it. The side-effecting tool must enforce the rule every time it is called.
Typical tools that deserve a gate include:
- Sending email, chat, SMS, social posts, or other outbound messages.
- Purchasing goods or services, creating an order, or changing a subscription.
- Making a bank transfer, card payment, refund, payout, or other money movement.
- Canceling an order, appointment, account, or service.
- Editing or deleting records, running shell commands, or invoking sensitive MCP actions.
Read-only tools such as searching a knowledge base or checking an account balance usually do not need the same interruption, although your policy may still protect sensitive data. Separate read access from write access so a low-risk lookup cannot be used to smuggle an unreviewed side effect through a different path.
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How does an approval-gated agent run work?
The run should move through a predictable six-stage lifecycle. The tool call is proposed first, but it is not executed until the approval decision is known.
- The model proposes a tool call. The call contains the tool name and arguments, such as a recipient, message body, currency, amount, order identifier, or destination account.
- The runner evaluates the rule. It applies an unconditional approval requirement or evaluates the arguments against thresholds, destinations, content rules, and other policy conditions.
- The runner interrupts before execution. When review is required, the side-effecting tool does not run. The run returns an interruption containing the proposed call and enough state to continue later.
- The application presents a review record. Show the reviewer the exact tool, every argument, the recipient or destination, the amount and currency, applicable limits, and the policy reason for the interruption.
- The reviewer approves or rejects. Store the decision with reviewer identity, timestamp, policy version, and a stable identifier for the proposed call.
- The application resumes the same run. Approval allows the original call to continue; rejection returns a controlled denial to the agent. Resume from preserved state instead of starting a fresh turn that could lose context or repeat a side effect.
OpenAI describes this pattern as human review that “pauses the run so a person or policy can approve or reject a sensitive action.” The pause is a control point, not merely a message to the model asking it to behave.
Should every message or payment require approval?
Use unconditional approval for irreversible or high-impact actions, and argument-dependent rules for actions that are safe only within defined limits. The rule should be evaluated on the fully formed tool arguments, after the model has produced the call but before the downstream API receives it.
When to use unconditional approval
- Transfers, refunds, or purchases that cannot be reliably reversed.
- Messages sent to external recipients when a mistaken disclosure would be serious.
- Account deletion, cancellation, or changes to legal, financial, or security settings.
- Tools that execute shell commands or invoke sensitive external systems.
An unconditional rule is easier to reason about: every call pauses, so a future change to the tool schema cannot silently create a bypass.
When argument-dependent approval is appropriate
Require review only when a call crosses a policy boundary. Useful conditions include:
- The amount exceeds a spending limit.
- The recipient is new, unverified, outside an allowlist, or in a different region.
- The currency is unusual for the account or business unit.
- The message contains sensitive, regulated, or confidential content.
- The purchase changes a recurring commitment or exceeds a quantity limit.
- The destination account, domain, or endpoint is not approved.
Keep the comparison and normalization logic in the tool policy. Parse amounts as precise decimal values, normalize currency and recipient identifiers, and reject malformed or missing arguments rather than letting a model-generated string decide whether review is necessary.
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What should the reviewer see before clicking approve?
The reviewer must be able to understand the exact effect without opening raw logs or guessing what an argument means. A compact summary is useful, but it must link to the canonical arguments that will be sent if approved.
- Action: the human-readable operation and the exact tool name.
- Arguments: the complete structured payload, including message text, item identifiers, quantities, and options.
- Recipient or destination: email address, phone number, account, merchant, URL, or service endpoint.
- Amount: numeric value, currency, fees if known, and the resulting total or limit check.
- Reason: which rule triggered review, such as “new recipient” or “amount over limit.”
- Run context: the user request, relevant conversation excerpt, and the agent or handoff that proposed the call.
- Identity: the user or service account under which the action will execute.
- Expiration and freshness: when the proposal was created and whether its quoted price, inventory, or authorization may have changed.
Do not allow the reviewer to approve a display-only summary while the executor uses a different payload. Bind the approval to a hash or immutable identifier for the exact arguments, then reject the call if those arguments change.
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How do I pause and resume the same run?
Use the framework’s interruption and resumable-state mechanism rather than issuing a new model turn. OpenAI’s SDKs expose approval interruptions and a run-state object; JavaScript and Python integrations surface approvals raised inside handoffs and nested agent-tool executions on the outer run.
- Persist the interruption object, run-state identifier, proposed tool call, and conversation context in durable storage.
- Create a review task keyed to that state. The task can remain pending while a person is offline.
- When the reviewer decides, persist the decision before attempting to continue the run.
- Reload the preserved state and resume the original top-level run with the approval or rejection result.
- After resumption, record whether the tool actually executed and the downstream provider’s response.
If review may take hours or days, treat the saved state as a durable workflow record, not an in-memory callback. Protect it with access controls and encryption appropriate to the message or payment data it contains.
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Return a structured denial to the run. The agent can explain that the action was not authorized, ask the user for a revised request, or continue with a non-side-effecting alternative. Do not silently convert a rejection into a modified call and execute it; a materially different recipient, amount, or message requires a new policy evaluation and, when applicable, a new approval.
What does fail-closed approval mean?
Fail closed means no side effect occurs unless a positive, verifiable approval decision is available. If the approval service is down, times out, returns malformed data, or cannot confirm the decision, reject or keep the call pending; never treat an outage as approval.
The Agent Approval Protocol documents deny-on-unconfirmed approval and at-most-once execution for approved calls. OpenAI’s Python SDK documentation likewise describes manual approval when arguments are missing, malformed, or not safely inspectable. These behaviors are especially important for money movement, where an uncertain status can be more dangerous than a delayed transaction.
- Use a short, explicit state machine such as
proposed,awaiting_approval,approved,rejected,executing,succeeded, andfailed. - Allow only valid state transitions, and make approval a one-way decision for a specific call identifier.
- Keep the executor from accepting an approval record that is expired, already consumed, or tied to different arguments.
- Alert on repeated approval-service failures instead of allowing an emergency bypass in production.
How should nested agents and handoffs be handled?
Approval must cover the actual tool execution even when another agent proposed it. A supervisor may hand work to a purchasing agent, or a customer-support agent may invoke a billing sub-agent; the outer application still needs to receive and resolve the interruption.
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OpenAI’s JavaScript and Python SDKs surface approvals raised inside handoffs and nested agent-tool executions on the outer run. Approve or reject at that outer boundary, then resume the original top-level run. Do not start a new conversation that asks the nested agent to “try again,” because it may lose the original context or issue a duplicate call.
Policy ownership in a multi-agent system
Define which layer owns each side-effecting tool. If both a supervisor and a child agent can call a payment API, put the final authorization check in the shared payment tool or service rather than relying on a prompt in either agent. Log the proposing agent, the handoff chain, and the identity that ultimately executed the call.
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How do I prevent duplicate messages or charges?
Approval and deduplication solve different problems. Approval decides whether a call is authorized; idempotency prevents a retry, timeout, or operator refresh from performing the authorized call twice.
Use an idempotency key or equivalent application-level deduplication mechanism around payment and message APIs. Derive it from a stable approval or action identifier, not from a newly generated random value on every retry. Store the key, payload hash, execution attempt, and provider result together so a retry can retrieve the original outcome.
The searched approval documentation establishes at-most-once behavior in the Agent Approval Protocol, but it does not define one universal idempotency implementation for every downstream provider. Your application remains responsible for checking the provider’s semantics and designing a safe recovery path when a network timeout leaves execution status uncertain.
Should I use a native framework control or a provider-mediated approval layer?
Choose the native control when one framework already provides approval interrupts and durable run state. Choose a provider-mediated layer when several frameworks or teams need one policy and audit boundary. The trade-offs are summarized below.
| Approach | Best fit | What it provides | What to verify |
|---|---|---|---|
| Native framework control | Applications built primarily on one agent SDK | Needs-approval rules, interruption handling, and resumable state such as RunState | Coverage of handoffs, persistence behavior, and how failures are surfaced |
| Microsoft approval-mode tools and middleware | Applications using Microsoft’s agent tooling | Approval-mode tools and middleware for inserting a review step | Which tools are covered, how state is resumed, and how identity is recorded |
| Agent Approval Protocol layer | Multiple frameworks or a centralized policy team | A provider boundary where every tool call is checked before execution, with adapters for several frameworks | Framework coverage, data handling, pricing, program terms, outage behavior, and audit retention |
| Custom application middleware | Teams with unusual tools or strict internal controls | Policy, reviewer workflow, storage, and downstream execution designed for your environment | Correct fail-closed behavior, nested-call coverage, durable state, and duplicate protection |
A provider-mediated layer can simplify consistent auditing, but it adds a dependency and a data boundary. Verify what call arguments and conversation context leave your environment, how long records are retained, and what happens when the provider cannot be reached.
What does a minimal implementation look like?
The following pseudocode shows the control flow without tying it to one vendor’s exact API names. The important property is that the side-effecting function is unreachable until the approval result is verified.
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result = run_agent(request)
while result.is_interrupted:
call = result.proposed_tool_call
policy = evaluate_policy(call)
if policy.requires_approval:
save_state(result.state, call, policy.reason)
decision = wait_for_reviewer(call, policy)
if decision.status != "approved":
result = resume(result.state, rejection(decision))
continue
if decision.call_id != call.id or decision.payload_hash != hash(call.arguments):
result = resume(result.state, rejection("stale or changed call"))
continue
result = resume(result.state, approval(call.id))
if result.requests_execution:
execute_once(call, idempotency_key=call.id)
In production, the executor should be the only component with credentials for the downstream message or payment API. The model, reviewer UI, and policy service should not be able to bypass that executor by calling the provider directly.
How should approvals be audited?
Record enough information to reconstruct who authorized what, under which policy, and what actually happened. An audit entry should include:
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- A unique action and run identifier.
- Tool name and a tamper-evident copy or hash of the arguments.
- Proposing agent, user or service identity, and handoff chain.
- Policy rule and version that triggered review.
- Reviewer identity, decision, timestamp, and any required comment.
- Execution attempts, idempotency key, downstream request identifier, and final outcome.
- State transitions, including rejection, timeout, cancellation, or provider outage.
Keep sensitive message bodies and financial details access-controlled. Audit logs are operational records, not a reason to expose private content to every administrator. Define retention and deletion rules that match your legal and security requirements.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do I test an approval gate before production?
Test the policy and the workflow separately, then test them together with a mocked or sandboxed downstream service.
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- An amount exactly at the limit and one unit above it.
- Known and unknown recipients or destinations.
- Normal and unusual currencies.
- Messages with and without sensitive content.
- Missing, malformed, negative, or ambiguous arguments.
- Calls made by a child agent, a handoff, and a direct top-level agent.
Workflow tests
- Reviewer approval resumes the same run and executes once.
- Reviewer rejection prevents execution and returns a controlled result.
- Approval-service timeout leaves the call unexecuted.
- Restarting the worker reloads the saved state and does not create a second call.
- A changed payload or expired decision is rejected.
- A downstream timeout can be reconciled without blindly retrying a payment or message.
Inspect provider logs and your own audit trail after every scenario. A green model response is not proof that the side effect was safely controlled; verify the actual number of downstream executions.
What are the most common implementation mistakes?
Putting the check in the prompt
Prompts can influence behavior but cannot enforce authorization. A tool-level gate remains effective when the model changes its wording or a different agent invokes the tool.
Showing incomplete arguments
If the reviewer cannot see the final recipient, amount, or message body, the decision is not informed. Render the exact payload that will be executed and bind it to the approval record.
Failing open during an outage
An unavailable approval service must not become an implicit approval. Deny or hold the call until a verifiable decision exists.
Starting a new run after review
A new turn can lose context and repeat work. Persist and resume the original state, including nested-agent interruptions.
Ignoring retries
Even a correctly approved call can execute twice after a network failure unless you use idempotency or an equivalent deduplication design.
Assuming one policy covers every tool
Authorization checks do not automatically propagate to every nested or handed-off tool. Inventory all paths to the side effect and enforce the final check at the shared execution boundary.
What is the practical decision rule?
For a small application using one SDK, start with its native approval interrupt, needs-approval rule, and resumable state. Add durable storage, a reviewer screen, fail-closed handling, and idempotency before enabling real messages or money movement.
For a larger organization with multiple frameworks, centralize policy and audit through a provider-mediated boundary or shared internal service. Whichever route you choose, require the same invariants: no execution before approval, no approval without inspectable arguments, no execution on an unconfirmed decision, durable resume of the original run, coverage of nested agents, and protection against duplicate calls.
What should you do before enabling real spending?
- Inventory every tool and API that can send, purchase, transfer, refund, cancel, edit, delete, or execute commands.
- Assign each tool an unconditional or argument-dependent approval rule.
- Implement the gate inside the side-effecting tool or shared executor.
- Build a reviewer view that shows exact arguments, destination, amount, limits, context, and policy reason.
- Persist interruption state and resume the original run after the decision.
- Configure deny-on-unconfirmed behavior for outages, malformed arguments, and stale approvals.
- Add idempotency keys or an equivalent deduplication mechanism for downstream calls.
- Test direct, nested, handed-off, rejected, retried, and provider-outage scenarios in a sandbox.
- Enable production credentials only after the audit trail proves that every approved action executes once and every other path remains blocked.
Frequently Asked Questions
Can a reviewer approve several agent actions at once?
Only if your policy explicitly defines a bounded batch approval. Bind the decision to a fixed list of action identifiers and payload hashes; any added, removed, or changed call should return to review.
What if the price or inventory changes while a purchase waits for approval?
Treat the original proposal as stale when the downstream system can change material terms. Re-fetch the quote or availability, create a new payload, and run policy evaluation again rather than executing an old approval against new conditions.
Do approval records need to contain the full message text?
Not always. You can store a protected copy, a content hash, or a redacted representation when privacy rules require minimization, but the reviewer must still be able to inspect the actual content before approving a send.
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Use a separately audited break-glass policy with named roles, a short validity window, and mandatory reason capture. It should still produce an approval event and remain subject to the same argument binding and duplicate-execution controls.
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